title: Non-parametric classification of esophagus motility by means of neural networks.
abstract: Automatic long-term recording of esophageal pressures by means of intraluminal transducers is used increasingly for evaluation of esophageal function. Most automatic analysis techniques are based on detection of derived parameters from the time series by means of arbitrary rule-based criterions. The aim of the present work has been to test the ability of neural networks to identify abnormal contraction patterns in patients with non-obstructive dysphagia (NOBD). Nineteen volunteers and 22 patients with NOBD underwent simultaneous recordings of four pressures in the esophagus for at least 23 hours. Data from 21 subjects were selected for training. The performances of two trained networks were subsequently verified on reference data from 20 subjects. The results show that non-parametric classification by means of neural networks has good potentials. Back propagation shows good performance with a sensitivity of 1.0 and a specificity of 0.8.
authors.affiliation: Department of Thoracic and Cardiovascular Surgery, Odense University Hospital, Denmark. calle@dou.dk
authors.forename: C
authors.initials: C
authors.lastname: Thøgersen
authors.forename: C
authors.initials: C
authors.lastname: Rasmussen
authors.forename: K
authors.initials: K
authors.lastname: Rutz
authors.forename: E
authors.initials: E
authors.lastname: Jakobsen
authors.forename: S
authors.initials: S
authors.lastname: Kruse-Andersen
mesh_terms: D000328:Adult; D000368:Aged; D003680:Deglutition Disorders / Q000209:etiology; D015154:Esophageal Motility Disorders / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D004947:Esophagus / Q000503:physiopathology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory; D016571:Neural Networks, Computer; D011312:Pressure; D012815:Signal Processing, Computer-Assisted